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CORRECTING FOR MEASUREMENT ERROR IN LATENT VARIABLES USED AS PREDICTORS.

Lynne Steuerle Schofield1

  • 1Swarthmore College, Department of Mathematics and Statistics.

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|March 16, 2016
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Summary

New math and personality insights reveal factors influencing STEM retention for underrepresented groups. These findings can inform interventions to boost persistence in science, technology, engineering, and mathematics (STEM) fields.

Keywords:
Primary Structural equations modelsSTEM retentionhigher educationitem response theory

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Area of Science:

  • Educational Research
  • Psychology
  • Sociology

Background:

  • Racial and gender disparities persist in Science, Technology, Engineering, and Mathematics (STEM) fields.
  • Existing models may not fully account for measurement error in latent variables predicting STEM retention.
  • Understanding factors influencing STEM persistence is crucial for developing effective interventions.

Purpose of the Study:

  • Introduce a novel methodological approach, the Mixed Effects Structural Equations (MESE) model.
  • Investigate the roles of pre-college mathematics proficiency and personality in STEM retention.
  • Quantify the contribution of these factors to racial and gender disparities in STEM.

Main Methods:

  • Developed the Mixed Effects Structural Equations (MESE) model, integrating structural equations modeling and item response theory.
  • Utilized data from the 1997 National Longitudinal Survey of Youth (NLSY).
  • Applied the MESE model to analyze predictors of STEM retention in higher education.

Main Results:

  • Prior mathematics proficiency and personality traits were found to be significant, previously underestimated predictors of STEM retention.
  • These factors collectively explain substantial portions of the observed racial and gender gaps in STEM persistence.
  • The MESE model effectively addressed measurement error bias in generalized linear models.

Conclusions:

  • Pre-college mathematics proficiency and personality are critical determinants of STEM retention.
  • Interventions aimed at increasing STEM persistence should consider these factors, particularly for women and under-represented minorities.
  • The MESE model offers a robust framework for analyzing complex relationships in educational research.